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from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any, Dict, List
from .operator import SingleStreamOperator, StreamInstanceOperator
from .stream import Stream
def absrtact_factory():
return {}
def abstract_field():
return field(default_factory=absrtact_factory)
class UpdateStream(StreamInstanceOperator):
update: dict
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
instance.update(self.update)
return instance
class Metric(ABC):
@property
@abstractmethod
def main_score(self):
pass
class GlobalMetric(SingleStreamOperator, Metric):
def process(self, stream: Stream):
references = []
predictions = []
global_score = {}
instances = []
for instance in stream:
if "score" not in instance:
instance["score"] = {"global": global_score, "instance": {}}
else:
global_score = instance["score"]["global"]
refs, pred = instance["references"], instance["prediction"]
instance_score = self._compute([refs], [pred])
instance["score"]["instance"].update(instance_score)
references.append(refs)
predictions.append(pred)
instances.append(instance)
result = self._compute(references, predictions)
global_score.update(result)
for instance in instances:
instance["score"]["global"] = global_score
yield instance
def _compute(self, references: List[List[str]], predictions: List[str]) -> dict:
result = self.compute(references, predictions)
result["score"] = result[self.main_score]
return result
@abstractmethod
def compute(self, references: List[List[str]], predictions: List[str]) -> dict:
pass
class InstanceMetric(SingleStreamOperator, Metric):
implemented_reductions: List[str] = field(default_factory=lambda: ["mean"])
@property
@abstractmethod
def reduction_map(self) -> dict:
pass
def process(self, stream: Stream):
global_score = {}
instances = []
for instance in stream:
refs, pred = instance["references"], instance["prediction"]
instance_score = self._compute(refs, pred)
if "score" not in instance:
instance["score"] = {"global": global_score, "instance": {}}
else:
global_score = instance["score"]["global"]
instance["score"]["instance"].update(instance_score)
instances.append(instance)
for reduction, fields in self.reduction_map.items():
assert (
reduction in self.implemented_reductions
), f"Reduction {reduction} is not implemented, use one of {self.implemented_reductions}"
if reduction == "mean":
from statistics import mean
for field in fields:
global_score[field] = mean([instance["score"]["instance"][field] for instance in instances])
if field == self.main_score:
global_score["score"] = global_score[field]
for instance in instances:
yield instance
def _compute(self, references: List[List[str]], predictions: List[str]) -> dict:
result = self.compute(references, predictions)
result["score"] = result[self.main_score]
return result
@abstractmethod
def compute(self, references: List[str], prediction: str) -> dict:
pass
class SingleReferenceInstanceMetric(InstanceMetric):
def _compute(self, references: List[str], prediction: str) -> dict:
result = self.compute(references[0], prediction)
result["score"] = result[self.main_score]
return result
@abstractmethod
def compute(self, reference, prediction: str) -> dict:
pass
class Accuracy(SingleReferenceInstanceMetric):
reduction_map = {"mean": ["accuracy"]}
main_score = "accuracy"
def compute(self, reference, prediction: str) -> dict:
return {"accuracy": float(str(reference) == str(prediction))}
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